Senior AI Engineer

MyNaksh; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

MyNaksh is seeking a Senior AI Engineer in Bengaluru to build production-grade AI systems focusing on conversational AI, personalization, memory, and recommendations. You will work with LLMs, RAG, AI agents, and personalization systems to deliver deeply personalized conversations at scale.

You will own AI features end-to-end, collaborating with Product, Backend, and Data teams, and will deploy scalable AI services with an AI-first mindset.

Qualifications

• 3–5 years of experience in ML, applied ML, NLP, or generative AI engineering.
• Strong proficiency in Python.
• Hands-on experience with LLMs, RAG, embeddings, vector search, and conversational AI.
• Experience deploying AI/ML systems into production.
• Strong understanding of ML fundamentals, evaluation, and experimentation.
• Experience with APIs, system design, scalability, and cloud platforms.
• Ability to own AI features end-to-end (design, build, evaluate, deploy).
• Strong software engineering fundamentals with an AI-first mindset.

Responsibilities

• Build and deploy production-grade AI/ML systems end-to-end.
• Develop LLM, RAG, conversational AI, and agentic workflows.
• Build orchestration pipelines for AI agents and LLM applications.
• Optimize AI systems for quality, latency, reliability, and cost.
• Deploy AI models and services to production environments.
• Build scalable APIs and backend services for AI applications.
• Design evaluation frameworks and monitoring for LLM performance.
• Work with vector search, embeddings, and retrieval pipelines.
• Partner with Product, Backend, and Data teams to ship AI-powered features.

Skills

Python

LLMs / Conversational AI

RAG

Embeddings

Vector search

NLP

Production ML

Cloud platforms

Tools

LangChain

LangGraph

Pinecone

Weaviate

Chroma

Milvus

Hugging Face Transformers

Job description

We are looking for a Senior AI Engineer to build production-grade AI systems focused on conversational AI, personalization, memory, recommendations, and long-term user intelligence. You will work on LLMs, RAG, AI agents, and personalization systems that help AI build persistent context and deliver deeply personalized conversations at scale.

The core responsibilities for the job include the following:

AI and LLM Engineering:

• Build and deploy production-grade AI/ML systems end-to-end.
• Develop LLM, RAG, conversational AI, and agentic workflows.
• Build orchestration pipelines for AI agents and LLM applications.
• Optimize AI systems for quality, latency, reliability, and cost.

Personalization and User Intelligence:

• Build memory systems and long-term conversational context.
• Develop recommendation and personalization models.
• Combine structured domain knowledge with LLM reasoning and retrieval.
• Improve user intelligence through embeddings, retrieval, and conversational history.

Production AI Systems:

• Deploy AI models and services to production environments.
• Build scalable APIs and backend services for AI applications.
• Design evaluation frameworks and monitoring for LLM performance.
• Work with vector search, embeddings, and retrieval pipelines.

Cross-Functional Collaboration:

• Partner with Product, Backend, and Data teams to ship AI-powered features.
• Own AI features from design through production deployment and monitoring.

Requirements:

• 3-5 years of experience in ML, applied ML, NLP, or generative AI engineering.
• Strong proficiency in Python.
• Hands-on experience with LLMs, RAG, embeddings, vector search, and conversational AI.
• Experience deploying AI/ML systems into production.
• Strong understanding of ML fundamentals, evaluation, and experimentation.
• Experience with APIs, system design, scalability, and cloud platforms.
• Ability to own AI features end-to-end (design, build, evaluate, deploy).
• Strong software engineering fundamentals with an AI-first mindset.
• Ownership of production AI systems.
• Passion for conversational AI and personalization.
• Ability to solve ambiguous product and engineering problems.
• Experience building scalable AI applications for real users.

Good to Have:

• LangChain or LangGraph.
• Vector databases (Pinecone, Weaviate, Chroma, Milvus, etc. ).
• Hugging Face Transformers and open-source LLMs.
• MLOps and LLM evaluation frameworks.
• Recommendation systems and personalization engines.
• Indic or multilingual NLP experience.